Transportation arrangement system utilizing artificial intelligence

ABSTRACT

A method, computer system, and computer program product for arranging transportation are provided. The method includes analyzing a first set of data by one or more processors to predict transportation demands of a passenger, and includes analyzing a second set of data by the one or more processors to determine a supply of vehicles in a transportation system. The method also includes generating, by the one or more processors, a transportation offer based on the predicted transportation demands of the passenger and the supply of vehicles. The method includes transmitting the transportation offer to the passenger and the transportation system. Upon receiving an acceptance of the offer by at least the passenger, the method further includes creating a transportation arrangement between the passenger and the transportation system.

BACKGROUND

Related transportation arrangement systems rely on vehicles having adriver, and also rely on passengers that input a desired destination. Incertain systems, a passenger makes a request for transportation to alocation, and the transportation arrangement system creates an offerthat is presented to a plurality of drivers within a vehicle fleet. Thedrivers can review the passenger's offer and accept the offer if they sochoose. This requires active input by both the passengers and thedrivers. Furthermore, related transportation systems that utilize avehicle having a driver require the passengers and the vehicle fleet toenter data into a common centralized computing system to arrangetransportation. With related driver-based transportation systems, thereare limitations such as efficiency, time and effort required to arrangea ride, costs, reliability, the inability to arrange transportationutilizing multiple vehicles for different portions of a trip, and a needfor both the passengers and the drivers of the vehicle fleet to enterdata into a common centralized server to determine a possible matchbetween supply and demand.

SUMMARY

The present disclosure relates generally to creating transportationarrangements between driverless vehicles and passengers based onartificial intelligence (“AI”). In particular, the present disclosurerelates to creating transportation arrangements between driverlessvehicles and passengers based on passenger data, and supply and demanddata within a transportation system, utilizing one or more artificialintelligence systems.

In certain embodiments, a method for arranging transportation between apassenger and a transportation system is provided. The method includesanalyzing a first set of data by one or more processors to predicttransportation demands of a passenger, and the method includes analyzinga second set of data by the one or more processors to determine a supplyof vehicles in a transportation system. The method also includesgenerating, by the one or more processors, a transportation offer basedon the predicted transportation demands of the passenger and the supplyof vehicles. The method includes transmitting the transportation offerto the passenger and the transportation system. Upon receiving anacceptance of the offer by at least the passenger, the method furtherincludes creating a transportation arrangement between the passenger andthe transportation system.

Other embodiments of the present disclosure are directed to a computersystem and computer program product for performing the method.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 depicts a block diagram of a processing system, according toembodiments.

FIG. 2 is a block diagram of an illustrative cloud computing environmenthaving one or more computing nodes with which local computing devicesused by cloud customers to communicate, according to embodiments.

FIG. 3 is a block diagram of a set of functional abstraction layersprovided by a cloud computing environment, according to embodiments.

FIG. 4 is a block diagram of a transportation arrangement system usingpassenger data to generate a predicted next destination for a passenger,and to generate a real time system demand map.

FIG. 5 is a table showing one example of historical travel data of apassenger.

FIG. 6 is a table showing one example of travel trends for a passengerthat is based on the historical travel data shown in FIG. 5.

FIG. 7 is a block diagram of a method for arranging transportationbetween passengers and vehicles in a transportation network.

DETAILED DESCRIPTION

The embodiments described herein provide for systems, methods andcomputer program products that use Big Data and Artificial Intelligence(AI) to facilitate transportation arrangements between passengers andvehicles. In certain embodiments, the transportation arrangements arebased on AI systems that model passenger needs and driverless vehicleavailability. In these embodiments, the transportation arrangementsystem generates matching offers based on this data, and then presentsthese offers to at least one of the passengers and a transportationnetwork including the driverless vehicles. If both the passenger and thetransportation network accept the matching offer, then thetransportation arrangement system creates the transportationarrangement.

Machine learning, which is a subset of AI, utilizes algorithms to learnfrom data (e.g., Big Data) and create foresights based on this data. AIrefers to the intelligence when machines, based on information, are ableto make decisions, which maximizes the chance of success in a giventopic. More specifically, AI is able to learn from a data set to solveproblems and provide relevant recommendations. AI is a subset ofcognitive computing, which refers to systems that learn at scale, reasonwith purpose, and naturally interact with humans. Cognitive computing isa mixture of computer science and cognitive science. Cognitive computingutilizes self-teaching algorithms that use data, visual recognition, andnatural language processing to solve problems and optimize processes.

As used herein, “Big Data” refers to data that is characterized, inpart, by large volumes of data (e.g., terabytes, petabytes, etc. insize), a large variety of data (e.g., including structured data,unstructured data, etc.), and different sources of data, etc. An exampleof structured data is transactional data in a relational database.Examples of unstructured data include images, email data, sensor data,etc. Some examples for sources of Big Data include banking information,travel information, medical records, geographical information,transportation system data, passenger data, etc.

As used herein, a “Smart City” generally refers to a metropolitan areathat utilizes different types of Big Data, and is collected from avariety of citizens, electronic Internet of Things (IoT) sensors, andother devices. The information is processed and analyzed to monitor andmanage different aspects of metropolitan infrastructure such as trafficand transportation systems, power plants, water supply networks, wastemanagement, police and fire departments, information systems, schools,libraries, hospitals, community services, etc. The data may be used tooptimize the efficiency of city operations and services, such as thetransportation systems that include the driverless vehicle fleetsdiscussed herein.

As used herein, “navigation” is defined as the process of planning aroute or directing travel of an object. A global positioning system(GPS) is a navigational system that uses satellite signals to determinelatitude and longitude of a receiver on Earth. The GPS has evolved inrecent years, and it is commonly found in land vehicles and smartphonedevices. Most current GPS systems utilize a visual display to present amap, a position on the map, and in some circumstances, directionsassociated with a requested navigation query. GPS systems are commonlyemployed to provide directions from a start location to an end location.The directions may be output through an interface, or in someconfigurations a microphone. The visual display is utilized to presentone or more images directed at the navigation. In the event there is adeviation from the generated route, instructions are re-calculatedand/or re-generated based on changes in position and associated positiondata. In the present embodiments, navigational systems such as a GPSsystem can be used in conjunction with the transportation arrangementsystems discussed herein to determine and optimize passenger and vehiclefleet routes from an origin to a destination.

Regarding the passengers of the driverless vehicles discussed herein,Big Data may include information about past transportation usagepatterns, and historical location information of passengers. Pasttransportation usage patterns include data about where certainpassengers have traveled to and from, and when they travelled. This mayinclude GPS location data with timestamp information reflecting originand destination locations, as well as the distances travelled betweenorigin and destination. The data of the past transportation usagepatterns may also include information regarding the type (or mode) oftransportation (e.g., personal car, taxicab, train, plane etc.) that wasused. The data may also include timestamp data showing when a passengerdeparted from a specific location, and when a passenger arrived at aspecific location.

Big Data location information for a passenger may include historicalinformation regarding where the passenger was at a given time, withoutregard to where the passenger was travelling to or from. For example, ifa passenger was at a grocery store at 7:00 am, the data may includeinformation such as the GPS coordinates of the location, the name of thestore, the address, the cross streets, and any other suitable metadatadata associated with the location.

Also, the Big Data for the passenger may include individual profileinformation, such as the time the passenger normally wakes up or fallsasleep, and what the time the passenger leaves for work or comes homefrom work, and what days of the week the passenger works, etc. Incertain embodiments, the passenger can specify what types of personalinformation they would like to share or keep private with thetransportation arrangement system.

For passengers, the transportation arrangement system applies artificialintelligence models to the passenger Big Data to predict where they arelikely to be travelling next. That is, from the data, the transportationsystem is able to identify patterns in passenger travel behavior thatallows the system to predict future travel needs. These predictionslessen or eliminate the need for the passenger to manually input theirdesired destination information. Thus, compared to relatedtransportation systems, it is more efficient and requires less effortfrom the standpoint of the user. In certain embodiments, if theprediction does not match the actual next desired destination of thepassenger, the passenger has the option to override the predicted nextdestination, or to simply reject (or ignore) an offer of transportation.

In certain embodiments, regarding a driverless vehicle fleet in a SmartCity, a transportation management system that controls the vehicle fleetapplies artificial intelligence models to the Big Data to optimize whereand when the vehicles should be operated, and to determine the demandlevel for vehicles at particular times and locations. These locationsmay be based on population movements, past customer demand, local events(e.g., concerts, or sporting events), historical trends, the time ofday, the day of the week, the season the year, the weather, etc. Thus,Big Data can be used to determine the demand for vehicles at differentlocations and at different times. In certain embodiments, the driverlessvehicle includes an onboard artificial intelligence system that canprocess Big Data. In other embodiments, the artificial intelligencesystem is on one or more computing systems that are separate from thevehicle.

Thus, as described above, Big Data is the raw input of large amounts ofvaried information. This Big Data may be gathered with respect to thepassengers, and with respect to a driverless vehicle fleet. In certainembodiments, an AI-enabled computing system analyzes and interprets bothsets of Big Data with computing models to determine output that aids inarranging transportation between the passengers and the driverlessvehicles.

Referring now to the drawings in which like numerals represent the sameor similar elements and initially to FIG. 1, an exemplary processingsystem 100 to which the present embodiments may be applied is shown inaccordance with one embodiment. The processing system 100 includes atleast one processor (CPU) 104 operatively coupled to other componentsvia a system bus 102. A cache 106, a Read Only Memory (ROM) 108, aRandom-Access Memory (RAM) 110, an input/output (I/O) adapter 120, asound adapter 130, a network adapter 140, a user interface adapter 150,and a display adapter 160, are operatively coupled to the system bus102.

A first storage device 122 and a second storage device 124 areoperatively coupled to system bus 102 by the I/O adapter 120. Thestorage devices 122 and 124 may be any of a disk storage device (e.g., amagnetic or optical disk storage device), a solid-state magnetic device,and so forth. The storage devices 122 and 124 may be the same type ofstorage device or different types of storage devices.

A speaker 132 is operatively coupled to system bus 102 by the soundadapter 130. A transceiver 142 is operatively coupled to system bus 102by network adapter 140. A display device 162 is operatively coupled tosystem bus 102 by display adapter 160.

A first user input device 152, a second user input device 154, and athird user input device 156 are operatively coupled to system bus 102 byuser interface adapter 150. The user input devices 152, 154, and 156 maybe any of a keyboard, a mouse, a keypad, an image capture device, amotion sensing device, a microphone, a device incorporating thefunctionality of at least two of the preceding devices, or any othersuitable types of input devices. The user input devices 152, 154, and156 may be the same type of user input device or different types of userinput devices. The user input devices 152, 154, and 156 are used toinput and output information to and from system 100. In certainembodiments, an artificial intelligence (AI) component 170 isoperatively coupled to system bus 102.

The processing system 100 may also include other elements (not shown),as readily contemplated by one of skill in the art, as well as omitcertain elements. For example, various other input devices and/or outputdevices may be included in processing system 100, depending upon theparticular implementation of the same, as readily understood by one ofordinary skill in the art. For example, various types of wireless and/orwired input and/or output devices can be used. Moreover, additionalprocessors, controllers, memories, and so forth, in variousconfigurations can also be utilized as readily appreciated by one ofordinary skill in the art. These and other variations of the processingsystem 100 are readily contemplated by one of ordinary skill in the artgiven the teachings of the present disclosure provided herein.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service.

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 2, illustrative cloud computing environment 250 isdepicted. As shown, cloud computing environment 250 includes one or morecloud computing nodes 210 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 254A, desktop computer 254B, laptop computer 254C,and/or automobile computer system 254N may communicate. Nodes 210 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 250 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 254A-Nshown in FIG. 2 are intended to be illustrative only and that computingnodes 210 and cloud computing environment 250 can communicate with anytype of computerized device over any type of netwo9+8rk and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 250 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 360 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 361;RISC (Reduced Instruction Set Computer) architecture-based servers 362;servers 363; blade servers 364; storage devices 365; and networks andnetworking components 366. In some embodiments, software componentsinclude network application server software 367 and database software368.

Virtualization layer 370 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers371; virtual storage 372; virtual networks 373, including virtualprivate networks; virtual applications and operating systems 374; andvirtual clients 375.

In one example, management layer 380 may provide the functions describedbelow. Resource provisioning 381 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 382provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 383 provides access to the cloud computing environment forconsumers and system administrators. Service level management 384provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 385 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 390 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 391; software development and lifecycle management 392;virtual classroom education delivery 393; data analytics processing 394;transaction processing 395; and artificial intelligence-basedtransportation arrangement processing 396.

Referring now to FIG. 4, in certain embodiments, the transportationarrangement system 400 utilizes passenger-based Big Data to generate anestimated demand for the entire transportation system. For a firstpassenger (or user) 412, the transportation arrangement system 400utilizes passenger data such as current passenger GPS location data 402,a passenger schedule 404 (e.g., restaurant bookings, doctorappointments, train bookings, airplane bookings, etc.), historicalpassenger travel data 406, a passenger's current time zone 408,historical route information for other passengers 416 having similartravel patterns 410, and any other suitable type of passenger travelinformation. Although several examples of passenger data are shown inFIG. 4, it should be appreciated that the transportation arrangementsystem 400 can utilize any other suitable type of data relating to apassenger or a passenger's travel habits. As shown in FIG. 4, all ofthis data is processed by an artificial intelligence module 414 (seealso, AI component 170 in FIG. 1) to predict what the passengers nextdestination is likely to be. The same information is processed for aplurality of other passengers 416. For the passenger 412 and the otherpassengers 416, the predicted next destinations are output to thetransportation arrangement system 400. Thus, the transportationarrangement system 400 has an overall view for all of the passengers inthe system as to what the likely demand for transportation will be at agiven point in time. This includes where passengers are at (or arelikely to be at), and where the passengers are likely to be travellingto next.

In one example, a first passenger has a job at a particular location,and regularly requests transportation from a first origin location to afirst destination location at a given time every morning (e.g., 7:00am). In this example, a second passenger also has a job at a particularlocation, and regularly requests transportation from a second originlocation to a first destination location at a given time every morning(e.g., 7:10 am). If the first and second origins locations are near toeach other, and the first and second destination locations are near toeach other, and because the departure times for the first and secondpassengers are close to one another, the transportation arrangementsystem 400 may determine that there is a relatively large transportationdemand at those places and times. In this case, the transportationarrangement system 400 may update the system demand map 418 in real timeto account for this demand, and may cause a greater supply of driverlessvehicles to be directed toward the vicinity of the origin location toaccount for this potential demand. It should be appreciated thatalthough the transportation arrangement system 400 is predicting thatthe passengers will travel to and from a given location at a given timebased on the analysis of the passenger data (e.g., 402, 404, 406, 408and 410), the passengers may not actually request travel arrangements.

As discussed above, in certain embodiments, the transportationarrangement system utilizes historical passenger travel data 406 to aidin predicting a next destination of the passenger. Referring now to FIG.5, this chart shows one nonlimiting example of a type of historicalroute data 500 of a passenger that may be utilized. As shown in FIG. 5,there are thirteen occasions when the passenger has used a driverlessvehicle to get from one location to another. The data includes the dateof travel, the time range of travel, a day of the week for travel, andGPS origin and destination information. For example, for Trip 1, thepassenger travelled on Date 1 which was a Monday, during a time range ofT1 to T2 (e.g., 7:00-8:00 am), and the passenger travelled from home towork. It should be appreciated that FIG. 5 is not limiting of the typeor amount of data that can be utilized, and other types of historicaldata could be utilized. For example, the transportation arrangement mayinclude other information associated with the GPS coordinates, such asstreet addresses, zip codes, cities, states, names of building orbusinesses at the location, etc. The date could also include exact timeof departure, and exact time of arrival rather than that the traveloccurred in a particular time range. Also, the time range could be, forexample, a particular hour of the day, an afternoon period, lateevening, rush hour, etc. It is from this raw data that thetransportation arrangement system utilizes the AI module to determinetrends and to attempt to predict future transportation needs of thepassengers located within the transportation network.

Referring now to FIG. 6, this table 600 shows one nonlimiting example ofhow the transportation arrangement system has analyzed the data from thetable in FIG. 5 to determine trends. For example, the passengertravelled from home to work consistently between the hours of T1 to T2every weekday (Monday through Friday). However, going home from work thepassenger only traveled on Tuesday, Thursday and Friday between thehours of T3 to T4. From this data, the transportation arrangement systemmay be able to identify a trend of travelling from home to work in themornings, and then automatically generate an offer to the passengerbased on this data. For example, on any given Monday, the transportationarrangement system would generate a notification to the user at time T1,and also provide an offer of transportation. By having thetransportation arrangement system automatically provide the offer to thepassenger, this has the effect of alleviating the need for the passengerto request transportation at this particular time. As discussed infurther detail below, the passenger can review the offer, and eitheraccept it, modify it, or reject it.

In certain embodiments, if the passenger reviews the offer and rejectsis (or doesn't respond to the offer), the transportation arrangementsystem determines whether there are additional passengers that may havesimilar needs. If the transportation system determines that there areother passengers with a potential travel need, they can transmit theoffer to one or more additional passengers, and these passengers thenhave the option to accept the offer.

Referring now to FIG. 7, this figure is a block diagram showing a methodfor arranging transportation 700 between passengers and vehicles in atransportation network, according to certain embodiments. In step 702,the transportation arrangement system (TAS) generates a real timeforecast analysis result that is based on the artificial intelligenceand Big Data discussed above. This real time forecast analysis resultincludes information regarding the supply of driverless vehicles in thetransportation network, and information regarding the predictedpassenger demands for transportation (e.g., the estimated transportationsystem demand generated in step 418 of FIG. 4).

In step 704, the transportation network receives the real time forecastanalysis result from the TAS. In step 706, the transportation network(e.g., a system directing or controlling a fleet of driverless vehicles)analyzes the real time forecast analysis result and the current supplyof driverless vehicles, and then generates one or more supply offers.For example, if the transportation network determines that there arevehicles in the vicinity of a certain location at a certain time, andthat there are one or more passengers that are predicted to have atransportation need in said place and time, then the transportationnetwork generates one or more offers of transportation.

In certain embodiments, in step 712, the passengers also receive thereal time forecast analysis result from the TAS. In one example, thepassengers have mobile computing devices with an application installedthereon that receives and processes the real time forecast analysisresult, and then displays certain results (e.g., a map of availablevehicles) to the passengers. In other embodiments, the passengers do notreceive these real time forecast analysis results.

In step 714, the TAS analyzes the real time forecast analysis result andthe predicted demand of the passengers, and then generates one or moredemand offers. In certain embodiments, these demand offers are based onthe predicted travel needs of passengers.

In step 716, the TAS compares the supply offers received from thetransportation network and the demand offers generated by the TAS, anddetermines whether there are any matches between the supply of vehiclesand the predicted passenger demand. If there are no matches, then theprocess returns to step 702 and the process is repeated with an updatedreal time forecast analysis result. In this case, the TAS has determinedthat the current supply of driverless vehicles does not match any of thepredicted passenger needs. If there are matches, then the processproceeds to step 718.

In step 718, in certain embodiments, the TAS presents the matchingoffers to both the passengers and the transportation network at the sametime. In these embodiments, in step 720, if at least one of thepassengers and the transportation network does not accept one of thematching offers, the process returns to step 702 and attempts to makenew matches with an updated real time forecast analysis result. In step720, if both the passenger and the transportation network accepts one ofthe matching offers, then the TAS creates a transportation arrangementbetween the passenger and one of the vehicles in the transportationnetwork. In this case, there is a successful match between the currentsupply of vehicles in the transportation network and the current demandsof the passengers.

In other embodiments, rather than presenting the matching offers to boththe passengers and the transportation network at the same time, the TASfirst presents the matching offer to just the passengers, and if apassenger accepts the matching offer, the matching offer is thenpresented to the transportation network. In these embodiments, thetransportation network can then accept or decline the matching offerthat has been previously accepted by the passenger.

In other embodiments, rather than presenting the matching offers to boththe passengers and the transportation network at the same time, the TASfirst presents the matching offer to just the transportation network,and if the transportation network accepts the matching offer, thematching offer is then presented to the passengers. In theseembodiments, the passengers can then accept or decline the matchingoffer that has been previously accepted by the transportation network.

In certain embodiments, the passenger reviews the offer on a graphicaluser interface (e.g., a software application on a mobile computingdevice such as a smart phone), and provides input regarding whether toaccept, modify or reject the offer. In certain embodiments, a softwareapplication provides the user with the ability to identify andcommunicate with other users in a similar location. For instance, inorder to reduce the overall cost for transportation, the passenger maybe able to share a ride with multiple other passengers. By ridesharing,all passengers are able to get from their origins to their destinationsat a fraction of the cost. In certain embodiments, the passenger caninteract with the mobile software application to view the locations ofother passengers in the vicinity who were also requesting transportationto a similar destination, and the application allows the passengers tocommunicate with one another via text messaging (or some other form ofgroup communication) in order to determine whether or not ridesharing isfeasible. For example, a plurality of different potential passengerscommunicate in a group text message thread to determine whether two ormore people can share the same vehicle. If the different passengersconclude that they would like to ride share, each of the passengers canaccept a single offer of transportation from the transportationarrangement system. Therefore, the transportation arrangement systemwould know that multiple passengers will be sharing the same vehicle. Inthis situation, where multiple different passengers may be at slightlydifferent origin locations, the transportation arrangement system candictate a central location for different passengers so as to minimizetotal travel distance for all passengers to get to the departuredestination. Alternatively, the multiple passengers can select a commonmeeting location, and provide this information to the transportationarrangement system and/or the transportation network. In certainembodiments, the transportation network receives the common meetinglocation from the multiple passengers, and then it can either acceptthis location or create an alternate meeting location.

In certain embodiments, a passenger provides information regarding atleast one of a present location of the passenger, a desired destination,a maximum desired cost of the transportation, a maximum number ofintermediate stops between the present location and the desireddestination, a maximum number of other passengers that the passengerwould be willing to travel with, and a maximum number of differentvehicles that the passenger is willing to take to reach the destination.

In certain embodiments, where the passenger has indicated that they arewilling to take multiple different vehicles to get to a desireddestination (e.g., to minimize total costs or travel time), thetransportation arrangement system presents an offer to the passengerthat includes information regarding these multiple legs of the trip. Incertain embodiments, the passenger would need to accept or reject theoffer as a whole. In other embodiments, the passenger can accept onlycertain legs of the trip, and the transportation arrangement systemwould provide alternative driving options for the missing legs for thepassenger's further review (i.e., to further accept or reject).

In certain embodiments, the present location of the passenger isautomatically tracked with a GPS enabled device (e.g., the passenger'scellular phone) without requiring any input from the passenger. Incertain embodiments, the passenger actively provides their presentlocation to the AI system. In certain embodiments, the passenger sets aschedule of where they are likely to be at given times on given days ofthe week or month. In other embodiments, the AI system analyzes pasttravel information of the passenger to predict where they are likely tobe at a given point in time. For example, if a passenger has a veryconsistent history of being at home at 7:00 am every Monday morning, andthen travelling to work at that time, the AI system generates aprediction that the passenger will most likely be at home at 7:00 am onMondays. In certain embodiments where the AI system generates aprediction of where the passenger will be at a given point in time, thepassenger has the option of overriding the predicted location. Forexample, if the passenger is on vacation (i.e., not at home at 7:00 amon a Monday morning), the passenger can provide an update to thepredicted present location with their actual present location. Also, asdiscussed above, in certain embodiments, the passengers have options tospecify how much (or how little) personal information and travel historyinformation they would like to share with the transportation arrangementsystem.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The descriptions of the various embodiments have been presented forpurposes of illustration and are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A method for arranging transportation, the methodcomprising: analyzing a first set of data by one or more processors topredict transportation demands of a passenger; analyzing a second set ofdata by the one or more processors to determine a supply of vehicles ina transportation system; generating, by the one or more processors, atransportation offer based on the predicted transportation demands ofthe passenger and the supply of vehicles; transmitting thetransportation offer to the passenger and the transportation system;upon receiving an acceptance of the offer by the passenger, creating atransportation arrangement between the passenger and the transportationsystem.
 2. The method of claim 1, wherein the first set of data includesat least one of: a current location of the passenger; historictransportation data of the passenger; a future schedule of thepassenger; and a time zone associated with the current location of thepassenger.
 3. The method of claim 2, wherein the historic transportationdata of the passenger includes data on past trips, each past tripincluding at least an origin location, an origin departure time, adestination location, and a destination arrival time.
 4. The method ofclaim 1, wherein the second set of data includes information regardingcurrent locations of a plurality of vehicles in the transportationsystem.
 5. The method of claim 1, wherein the vehicles are automateddriverless vehicles.
 6. The method of claim 1, wherein creating thetransportation arrangement between the passenger and the transportationsystem requires acceptance of the offer by the passenger and by thetransportation system.
 7. The method of claim 1, wherein upon rejectionof the offer by at least one of the passenger and the transportationsystem, the method further includes transmitting the transportationoffer to a second passenger and the transportation system.
 8. The methodof claim 1, wherein analyzing the first set of data and the second setof data is performed using an artificial intelligence system.
 9. Acomputer system comprising: a computer readable storage medium withprogram instructions stored thereon; and one or more processorsconfigured to execute the program instructions to perform a method forarranging transportation, the method comprising: analyzing a first setof data by the one or more processors to predict transportation demandsof a passenger; analyzing a second set of data by the one or moreprocessors to determine a supply of vehicles in a transportation system;generating, by the one or more processors, a transportation offer basedon the predicted transportation demands of the passenger and the supplyof vehicles; transmitting the transportation offer to the passenger andthe transportation system; upon receiving an acceptance of the offer bythe passenger, creating a transportation arrangement between thepassenger and the transportation system.
 10. The computer system ofclaim 9, wherein the first set of data includes at least one of: acurrent location of the passenger; historic transportation data of thepassenger; a future schedule of the passenger; and a time zoneassociated with the current location of the passenger.
 11. The computersystem of claim 10, wherein the historic transportation data of thepassenger includes data on past trips, each past trip including at leastan origin location, an origin departure time, a destination location,and a destination arrival time.
 12. The computer system of claim 9,wherein the second set of data includes information regarding currentlocations of a plurality of vehicles in the transportation system. 13.The computer system of claim 9, wherein the vehicles are automateddriverless vehicles.
 14. The computer system of claim 9, whereincreating the creating the transportation arrangement between thepassenger and the transportation system requires acceptance of the offerby the passenger and by the transportation system.
 15. The computersystem of claim 9, wherein upon rejection of the offer by at least oneof the passenger and the transportation system, the method furtherincludes transmitting the transportation offer to a second passenger andthe transportation system.
 16. The computer system of claim 9, whereinanalyzing the first set of data and the second set of data is performedusing an artificial intelligence system.
 17. A computer program productfor implementing a method for arranging transportation, the computerprogram product comprising a computer readable storage medium havingprogram instructions embodied therewith, the program instructionsexecutable by at least one computer processor to cause the computerprocessor to: analyze a first set of data by one or more processors topredict transportation demands of a passenger; analyze a second set ofdata by the one or more processors to determine a supply of vehicles ina transportation system; generate, by the one or more processors, atransportation offer based on the predicted transportation demands ofthe passenger and the supply of vehicles; transmit the transportationoffer to the passenger and the transportation system; upon receiving anacceptance of the offer by the passenger, create a transportationarrangement between the passenger and the transportation system.
 18. Thecomputer program product of claim 17, wherein the first set of dataincludes at least one of: a current location of the passenger; historictransportation data of the passenger; a future schedule of thepassenger; and a time zone associated with the current location of thepassenger.
 19. The computer program product of claim 18, wherein thehistoric transportation data of the passenger includes data on pasttrips, each past trip including at least an origin location, an origindeparture time, a destination location, and a destination arrival time.20. The computer program product of claim 17, wherein the vehicles areautomated driverless vehicles.